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Introduction to R for Quantitative Finance

An introductory book on Quantitative Finance and R I co-authored with some learned faculty members of the Corvinus University of Budapest (Michael Puhle, Edina Berlinger, Péter Csóka, Dániel Havran, Ferenc Illés, Tamás Makara, Márton Michaletzky, Zsolt Tulassay, Varadi Kata and Ágnes Vidovics-Dancs) has been recently published at Packt:

I really hope that this mini-book would be of assistance for those, who already have some experience with finance, but would also love to get familiar with the R language and to solve real-life quantitative finance problems with the help of the “lingua franca” of statistical analysis.

Although the book is intended to be rather practical and it focuses on a number of clear and practical examples and some R implementations, it also covers the essentials of the related finance materials at the beginning of each chapter. This also helped me a lot to grasp what’s going on in the background, as my role was to provide R code for the book without any former finance education 🙂

Nevertheless, the book covers a wide range of quantitative finance-related topics introduced and discussed by the faculty members of the Department of Finance at BCE, such as:

Time series analysis

Portfolio optimization

Asset pricing models

Fixed income securities

Term structure of interest rates

Derivative pricing

Credit risk models and management

Extreme value theory

Finance networks

The first chapter is available to download for free at the publisher’s home page, and I would be more than happy to ship a few (printed or electronic) copies for potential reviewers to get some feedback to see if the mini-book has really hit the target. Please mail me at daroczig {at} rapporter {dot} net for more details.

And some personal narrative: it was a pleasure to work on the R parts of this mini-book, and also to get to know and co-ordinate the author-team working on the theoretical background and providing real-world examples. Most of the time, my tasks were rather a piece of cake, like applying some functions from some CRAN packages, on the other hand sometimes I faced serious problems to understand what needs to get done and digged deep into package sources so that I could interpret the function to the co-authors and to see if the R tricks really do what we want to present in the chapter.

Some statistics: it took 6 months to write, review, iterate and to publish that cca. 150 pages and had more than 500 e-mails on the topic. I really hope you will enjoy the book equally if not more than I did!Update (12/1/2014): due to the high number of requests, I can no longer offer free copies of the book on my own.